Enabling distributed intelligence in Internet of Things: an air quality monitoring use case

Lazrak, Noussair; Ouarzazi, Jamal; Zahir, Jihad; Mousannif, Hajar

doi:10.1007/s00779-020-01483-3

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…The first dimension is support human, which accounts for 18.920% of the observed total variance, and it contains seven items: transfer knowledge via iots application, sharing knowledge through cloud platforms, support better decision-making, decision support system, educational tool (3D printing), complex geometric shapes and reduce product time. These findings were supported by the research of Lazrak et al (2020), Masuda et al (2021), and Baek and Chung (2021), who designed technology to facilitate of people's operations such as share knowledge between people, support decision-making processes and provide educational tools. The second dimension is labelled as automation (18.791% of variance) and contains eight items: auto-decision of a machine, knowledge extraction from IoT devices, cognitive knowledge of IoT devices, robots connected by cloud computing, artificial intelligence techniques, additive manufacturing robots, controlling IoT devices and promoting innovation.…”

Section: Discussionmentioning

confidence: 72%

The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

Sawangwong

Chaopaisarn

2021

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PurposeThe purpose of the study is to investigate the impact of technological pillars of Industry 4.0 based on knowledge to adopt the supply chain performance of Thai small and medium-sized enterprises (SMEs) 4.0. In addition, to increase knowledge and understanding of how to apply knowledge in technology 4.0 to improve the efficiency of supply chains and organizations.Design/methodology/approachAn integrated model was developed from applying knowledge in five technological pillars of Industry 4.0 such as Internet of things (IoTs), cloud computing, big data and analytics, additive manufacturing and cyber-security. The bibliometric analysis was used to find the relationship between the technological pillars of Industry 4.0 and the literature review. The survey questionnaires were sent to Thai SME 4.0 (manufacturing aspect). Of these, 240 useable responses were received, resulting in a response rate of 65.84%, after then, the exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM) and validity were used to evaluate the model through IBM SPSS 21 and AMOS 22.FindingsEFA showed the four groups of the technological pillars of Industry 4.0, such as support human, automation, real-time and security. These groups positively impact supply chain performance (increase delivery reliability, increase resource efficiency, decrease costs in the supply chain and reduce delivery time). Another important finding is that supply chain performance positively impacts organizational performance in profitability, return on investment (ROI) and sale growth.Originality/valueThis study is a model development to support the supply chain performance and increase understanding related to applying knowledge in technology 4.0 that remains unclear for SME 4.0.

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Section: Discussionmentioning

confidence: 72%

The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

Sawangwong

Chaopaisarn

2021

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“…For examples, Kok et al [30] and Alaoui et al [31] used criteria similar to the US, whereas Nashh et al [32] mentioned the US and European Standards. European Standards is also used in Lazrak et al [33]. Indian Standards are used in Moses et al [12] Monitoring and Re-routing system.…”

Section: Indian Air Monitoring Criteriamentioning

confidence: 99%

The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems Using IoT, Big Data, and Machine Learning

Gangwar

Singh

Mishra

et al. 2023

Wireless Pers Commun

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The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.

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“…Additionally, a distributed intelligence-based AQM model has been developed by L a z r a k et al [18], which measures the air quality in urban areas. This model uses node interoperability, collective learning, and shared knowledge to attain better results in monitoring and prediction.…”

Section: Related Workmentioning

confidence: 99%

Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN

Saritha,

Sarasvathi

2023

Cybernetics and Information Technologies

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Transmission of information is an essential component in an IoT device for sending, receiving, and collecting data. The Smart devices in IoT architecture are designed as physical devices linked with computing resources that can connect and communicate with another smart device through any medium and protocol. Communication among various smart devices is a challenging task to exchange information and to guarantee the information reaches the destination entirely in real-time in the same order as sent without any data loss. Thus, this article proposes the novel Bat-based Deep Belief Neural framework (BDBN) method for the air pollution monitoring scheme. The reliability of the proposed system has been tested under the error condition in the transport layer and is validated with the conventional methods in terms of Accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson correlation coefficient (r), Coefficient of determination (R2) and Error rate.

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Enabling distributed intelligence in Internet of Things: an air quality monitoring use case

Cited by 5 publications

References 31 publications

The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance

The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems Using IoT, Big Data, and Machine Learning

Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN

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